1887
Volume 37, Issue 9
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

Machine learning (ML) is being used to process and understand data more and more throughout geology and geophysics in oil and gas exploration. However, its uptake has been slower than it has been for other industries and within exploration has been much stronger for some types of data and disciplines than it has for others. For instance, there has been significant ML focus on seismic data, while other data, such as those generated by geochemistry, have received relatively little. This imbalance in ML uptake leaves much opportunity for the growth of ML applications in these undersaturated fields.

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2019-09-01
2019-12-10
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References

  1. Barstow, D.R.
    [1984]. Artificial Intelligence at Schlumberger. AI Mag., 5, 80–82.
    [Google Scholar]
  2. ter Braak, C.J.F., Hoijtink, H., Akkermans, W. and Verdonschot, P.F.M.
    [2003]. Bayesian model-based cluster analysis for predicting macrofaunal communities. Ecol. Model. 160, 235–248.
    [Google Scholar]
  3. Chollet, F.
    [2017]. Deep Learning with Python, Manning Publications, New York, USA.
    [Google Scholar]
  4. Chollet, F. and Allaire, J.J.
    [2018]. Deep Learning with R, Manning Publications, New York, USA.
    [Google Scholar]
  5. Digby, P.G.N. and Kempton, R.A.
    [1987]. Ordination. In: Digby, P.G.N. and Kempton, R.A. (Eds.) Multivariate Analysis of Ecological Communities. SpringerNetherlands, Dordrecht, 49–111.
    [Google Scholar]
  6. Dowle, M. and Srinivasan, A.
    [2019]. data.table: Extension of ‘data.frame’.
    [Google Scholar]
  7. McKinney, W.
    [2010]. Data Structures for Statistical Computing in Python. In: Van der Walt, S. and Millman, J. (Eds.) Proceedings of the 9th Python in Science Conference, 51–56.
    [Google Scholar]
  8. Nguyen, L.H. and Holmes, S.
    [2019]. Ten quick tips for effective dimensionality reduction. PLOS Comput. Biol., 15, e1006907.
    [Google Scholar]
  9. Ouyang, M., Welsh, W.J. and Georgopoulos, P.
    [2004]. Gaussian mixture clustering and imputation of microarray data. Bioinformatics, 20, 917–923.
    [Google Scholar]
  10. Stigler, S.M.
    [1981]. Gauss and the Invention of Least Squares. Ann. Stat., 9, 465–474.
    [Google Scholar]
  11. Wickham, H. and Henry, L.
    [2019]. tidyr: Easily Tidy Data with “spread()” and “gather()” Functions.
    [Google Scholar]
  12. Wickham, H., François, R., Henry, L.
    and Müller, K. [2019]. dplyr: A Grammar of Data Manipulation.
    [Google Scholar]
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